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Post-doc in digital design for efficient embedded machine learning processors - Belgium  

KU Leuven (company)


Posted on : 07 May 2017

Project Description

ResponsibilitiesSpecifically, the project is currently looking for a PhD or post-doc to on resource-efficient digital implementations of machine learning processors, focused around Bayesian machine learning (and more general: Probablistic Graphical Models), deep learning, and reinforcement learning.Recently deep neural networks, such as convolutional neural networks (CNNs) or longshort-term memory (LSTM) networks have gained enormous popularity in the signal processing community. In the micro-elecronics research domain this has sprouted attention on customized processors for efficient embedded deep neural network inference. Our team has published several of these state-of-the-art processors over the past few years.For the higher cognitive layers, where often sensor fusion takes place, a second machine learning paradigm is attractive: Bayesian learning and Probablistic Graphical Models. These techniques enable to more smoothly inject expert knowedge into the system, and reason about the sensed information. White such white box  classifiers are attractive from a knowledge point of view compared to the black box  deep neural networks, their execution is still very computationally intensive on traditional processors. And so far, no customized processors have been build for these workloads.With this project,we want to enable the power of Probablistic Graphical Models to embedded devices. This through custom processor design and hardwae accelerator design for both online learning and inference tasks. This research will hece require a combination of algorithmic innovations (dealing with reinforcement learning and Probablistic Graphical Models) and hardware innovations (processor design,low-power optimization and chip tape-out). We have already prooven in the field of Deep Learning Processors, that such hardware/software co-optimization allows to save orders of magnitude on energy efficiency. With this project, we want to achieve similar gains for the next emerging deep learning technology beyond CNNs, DNNs, and RNNs, and enable the power of Probablistic Graphical Models on embedded devices. In this project, we closely collaborate with researchers from KU Leuven s machine learning group DTAI, as well as with UCLA s machine learning group.Profile

Locations

3000 Leuven Belgium

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